Modular Acquisition and Stimulation System for Timestamp-Driven Neuroscience Experiments

TL;DR

This paper presents a modular, low-cost data acquisition and stimulation system for neuroscience experiments, optimized for timestamp recording and designed using high-level HDL for flexibility and rapid prototyping.

Contribution

It introduces a novel modular system implemented in high-level HDL, enabling flexible architecture design and rapid prototyping for neuroscience instrumentation.

Findings

Successfully implemented in a low-cost reconfigurable device

Compared two memory arbitration schemes for performance and resilience

Demonstrated ease of modifying architecture via HDL

Abstract

Dedicated systems are fundamental for neuroscience experimental protocols that require timing determinism and synchronous stimuli generation. We developed a data acquisition and stimuli generator system for neuroscience research, optimized for recording timestamps from up to 6 spiking neurons and entirely specified in a high-level Hardware Description Language (HDL). Despite the logic complexity penalty of synthesizing from such a language, it was possible to implement our design in a low-cost small reconfigurable device. Under a modular framework, we explored two different memory arbitration schemes for our system, evaluating both their logic element usage and resilience to input activity bursts. One of them was designed with a decoupled and latency insensitive approach, allowing for easier code reuse, while the other adopted a centralized scheme, constructed specifically for our application. The usage of a high-level HDL allowed straightforward and stepwise code modifications to transform one architecture into the other. The achieved modularity is very useful for rapidly prototyping novel electronic instrumentation systems tailored to scientific research.